Smooth textured surface reconstruction from point cloud rendered images as well as path traced images using variational methods

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Rendering and Modeling
• Date: July 2023
• Date (Start): 16. January 2023
• Date (End): 16. July 2023
• Diploma Examination: 28. July 2023
• First Supervisor:
  • Annalena Ulschmid
  • Michael Wimmer
  • Josef Stumpfegger
  • Christoph Neuhauser
  • Rüdiger Westermann

Abstract

In this work, we propose total variation-based methods for smoothing textured surfaces in point-based rendering and reducing noise in Monte Carlo-rendered images. Initially, we survey the challenges and existing state-of-the-art methodologies in these two research domains. Subsequently, we delve into the details of our proposed total variational models, each aimed at smoothing point-rendered textured surfaces and reducing noise in Monte Carlo-rendered images, respectively. For smoothing textured surfaces in point-based rendering, our model incorporates geometric features and is then combined with an advanced Pull-Push method. This combined approach enables us to effectively fill gaps and smooth discontinuous surfaces. The models tailored for denoising Monte Carlo-rendered images leverage noise-free auxiliary features and noise estimation techniques. Our approach efficiently eliminates noise while preserving crucial image features. We conduct comprehensive comparison experiments against existing state-of-the-art techniques to evaluate the effectiveness of our methods. Although our implementations are currently offline, both the smoothing and denoising processes can be achieved within a few iterations. Given the simplicity of our approach’s implementation, we foresee the potential for a GPU-based implementation, paving the way towards real-time applications.

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BibTeX

@mastersthesis{Liang-2023-TVR,
  title =      "Smooth textured surface reconstruction from point cloud
               rendered images as well as path traced images using
               variational methods",
  author =     "Han Liang",
  year =       "2023",
  abstract =   "In this work, we propose total variation-based methods for
               smoothing textured surfaces in point-based rendering and
               reducing noise in Monte Carlo-rendered images. Initially, we
               survey the challenges and existing state-of-the-art
               methodologies in these two research domains. Subsequently,
               we delve into the details of our proposed total variational
               models, each aimed at smoothing point-rendered textured
               surfaces and reducing noise in Monte Carlo-rendered images,
               respectively. For smoothing textured surfaces in point-based
               rendering, our model incorporates geometric features and is
               then combined with an advanced Pull-Push method. This
               combined approach enables us to effectively fill gaps and
               smooth discontinuous surfaces. The models tailored for
               denoising Monte Carlo-rendered images leverage noise-free
               auxiliary features and noise estimation techniques. Our
               approach efficiently eliminates noise while preserving
               crucial image features. We conduct comprehensive comparison
               experiments against existing state-of-the-art techniques to
               evaluate the effectiveness of our methods. Although our
               implementations are currently offline, both the smoothing
               and denoising processes can be achieved within a few
               iterations. Given the simplicity of our approach’s
               implementation, we foresee the potential for a GPU-based
               implementation, paving the way towards real-time
               applications.",
  month =      jul,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2023/Liang-2023-TVR/",
}